Asia-Pacific Journal of Operational Research

Manufacturers and suppliers offer temporary reductions (or permanent increases) in the price charged to the resellers for a variety of reasons. The trade promotion may be offered to the reseller at a single point in time or over a finite time-span. In addition, in reselling situations, the end demand tends to be sensitive to selling price. It is commonly found that under such trade promotion (or in stockpiling to an announced price increase), not all the quantity purchased by the reseller at discount may be passed on to the final consumer at a reduced selling price. In fact, previous studies have shown that it is optimal for the reseller to carry forward some of the quantity purchased at discount and sell it later at the regular price. It has been suggested in the literature that by placing a restriction on the reseller's purchase quantity, the supplier can restrict the reseller's forward buy quantity. In this paper, we evaluate this approach. In the rest of the paper, we present alternate schemes which are easy to administer and which insure that the supplier avoids the spike in demand that occurs in the unconstrained problem.

This paper considers quality of service in terms of end-to-end delay in planning backup routes for multicast communications. The problem of preplanning backup routes considering both cost minimization and end-to-end delay guarantee for multicast communications in the case a single link failure is investigated. Two delay labels, the limited label and the tolerable label, are defined to evaluate the end-to-end delay requirement. Four heuristic algorithms, including two tree-based algorithms, one subtree-based algorithm, and one link-based algorithm, are proposed to determine the delay-constrained backup routes having the minimum costs. Two procedures to determine a node selection sequence, Random and Minimum-Cost, are used in the tree-based algorithm. Experimental results show that the tree-based algorithm by the Minimum-Cost sequence yields the best performance in cost minimization with guarantee of end-to-end delay.

This paper considers two/three-machine no-wait flow shop scheduling problems with makespan minimization. Inherited with the NP-hard problem nature, this scheduling problem is solved by using a Simulated Annealing (SA) and Genetic Algorithm (GA) instead of mathematical programming. These two well-known search algorithms are often used to solve complex combinational optimization problems. In order to compare the performance of the two algorithms, we use solution quality (under the same computational time) and computation efficiency (under the same solution quality) as the measuring criteria. From the example problem, we found that SA is superior to GA in both solution quality and computation efficiency under identical terminating conditions. The performance of SA and GA decreased upon increasing the number of jobs. Our main contributions are to compare SA and GA under limited resources, which would be more consistent with the real world.

The analytic hierarchy process is combined with multi-objective mixed integer programming to determine the optimal allocation of a limited number of aircraft among a group of airlift users with varying levels of priority and length of usage. Canadian Forces airlift planners typically encounter such a capacity planning problem. The solution to this problem requires the constrained assignment of n variable length missions (tasks) integrating hundreds of airlift requests from several users with many priorities to m airframes (parallel machines).

In this paper, we consider a semi-online preemptive scheduling problem on two uniform machines, where we assume that all jobs have sizes between p and rp for some p > 0 and r ≥ 1. The goal is to maximize the continuous period of time (starting from time zero) when both machines are busy. We present an optimal semi-online algorithm for any combination of the job size ratio r and machine speed ratio s.

A model that integrates the issues of production, quality, maintenance, inspection, inflation, and time value of money is proposed in this paper. It is assumed that the production system starts in the in-control state producing items of high or perfect quality. However, the process may deteriorate with time and shifts at a random point in time to an out-of-control state and begins to produce non-conforming items. The elapsed time for the process to shift to the out-of-control state is assumed to follow a general distribution. Imperfect preventive maintenance, restoration, and inspection errors are taken into account. Also, inflation, time value of money, and shortage are considered in the proposed model. Numerical examples are presented to illustrate important aspects of the integrated model.

In this paper, by using a modified smoothing function, we propose a new continuation method for complementarity problems with R₀-function and P₀-function in the absence of strict complementarity. At each iteration, the continuation method solves one linear system of equations and performs one line search. When the underlying mapping is both a P₀-function and a R₀-function and its Hessian is Lipschitz continuous, we prove the global convergence of the new method. The new method also has global Q-linear and local Q-quadratical convergence rates under the same conditions.

This investigation adopts the perspective of the retailer and incorporates information flow between a retailer and customers. Two new models are considered that differ in terms of information completeness. The first model involves the retailer having incomplete information regarding the state of customers demand, namely extending the model of Dekker et al. (IIE Transactions, 32, 2000, 461) by considering the quadratic concave holding and penalty cost functions based on the law of diminishing marginal cost to fit in with the some practical situations. Meanwhile, the second model involves the retailer having full information on the state of customers demand. Precise expressions are derived for the expected total profit of these two newsboy models with a cutoff transaction size and compound Poisson demand distribution. It is worthwhile to measure the value of information and identify the effect of factors for enterprise decision making regarding whether or not to pay for information that can help increase profits. Moreover, we adopt and modify the golden section search technique (Haftka et al., 1990) to determine an optimal order-up-to level S and a cutoff transaction size q systematically. Finally, numerical examples are given to illustrate the result derived.